in recent decades, seizure prediction caused a lot of research in both signal processing and neuroscience field. the researches tried to enhance the conventional seizure prediction algorithms such that rate of the false alarms be appropriately small so that seizures can be predicted according to clinical standards. up to now none of the proposed algorithms have been sufficiently adequate. in th...

bivariate features, obtained from multichannel electroencephalogram (eeg) recordings, quantify the relation between different brain regions. studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. a new bivariate approach using univariate features is proposed here. differences and ratios ...

The unpredictability of seizures is a central problem for all patients suffering from uncontrolled epilepsy. Recently, numerous methods have been suggested that claim to predict from the EEG the onset of epileptic seizures. In parallel, new therapeutic devices are in development that could control upcoming seizures provided that their onset is known in advance. A reliable clinical application c...

Wong S, Gardner AB, Krieger AM, Litt B. A stochastic framework for evaluating seizure prediction algorithms using hidden Markov models. J Neurophysiol 97: 2525–2532, 2007. First published October 4, 2006; doi:10.1152/jn.00190.2006. Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they de...

Most of the current epileptic seizure prediction algorithms require much prior knowledge of a patient’s pre-seizure electroencephalogram (EEG) patterns. They are impractical to be applied to a wide range of patients due to a very high inter-individual variability of EEG patterns. This paper proposes an adaptive prediction framework, which is capable of accumulating knowledge of pre-seizure EEG ...